1. Tanya L. Otte and Robert C. Gilliam NOAA Air Resources Laboratory, Research Triangle Park, NC (In partnership with U.S. EPA National Exposure Research Laboratory) 6 th Annual CMAS Conference Chapel Hill, NC 2 October 2007 A Nudging Strategy for Mesobeta-Scale WRF Simulations Suitable for Retrospective Air Quality Modeling: Preliminary Results

2. What is “Nudging”? Formally, “Newtonian relaxation” Method of dynamically relaxing model toward observed state  Includes non-physical forcing term in prognostic equations  Uses difference between model and best estimate of observation in space and time Used throughout retrospective simulations (i.e., a “dynamic analysis”) to keep meteorology as close to observed as possible  Extends run length of “usable” meteorology for AQ modeling One method of FDDA in MM5 and WRF Typically applied to:  Horizontal wind components (U and V)  Temperature (T) above the PBL  Water vapor mixing ratio (Q) above the PBL

3. Motivation Current nudging strategies for MM5 and WRF typically based on four papers:  Stauffer and Seaman, MWR, 1990 Initial MM5 nudging paper; both analysis and obs nudging  Stauffer, Seaman, and Binkowski, MWR, 1991 Nudging in PBL…OK for wind, restrict for mass and moisture  Stauffer and Seaman, JAM, 1994 Multiscale approach to phase from analysis to obs nudging as horizontal grid spacing decreases  Seaman, Stauffer, and Lario-Gibbs, JAM, 1995 Define nudging coefficients for multiscale approach Use 36/12/4 km nesting for air quality modeling

4. Motivation (continued) Seaman et al. (1995) used input analyses derived from 12-h, 2.5-deg 3D analyses and 3-h, 2.5-deg surface analyses  Analysis to observations done in MM5 with “RAWINS” toward conventional surface observations and rawinsondes Current archived analyses are often as fine as 3-h, 12-km (e.g., NAM218) for 3D and surface  Analyses include combination of conventional and remote- sensed observations  Can use “RAWINS” with MM5; only 3DVar in WRF (for now) Is nudging strategy (coefficients and multiscale approach) in Seaman et al. (1995) too restrictive given today’s data availability?

5. WRFv2.2+ Setup Input AnalysesNARR (32-km) grid, 3-hourly Explicit MicrophysicsWSM 6 Convective ParameterizationKain-Fritsch PBL ModelACM2* Land-Surface ModelPleim-Xiu LSM* RadiationRRTM LW & Dudhia SW * Not in released code; to be included in WRFv3

6. WRF Domain 12 km, 290 x 251 x 34 layers

7. Sensitivity Overview Low: Typical nudging coefficients for 12-km  1.0 x 10 -4 s -1 for U,V,T; 1.0 x 10 -5 s -1 for Q  From Seaman et al., JAM, 1995 for analysis nudging with obs nudging @12-km Std: Typical nudging coefficients for 36-km  3.0 x 10 -4 s -1 for U,V,T,Q  ~1-h e-folding time for physical processes High: Nudging coefficients with half e-folding time of Std  5.5 x 10 -4 s -1 for U,V,T,Q  ~0.5-h e-folding time for physical processes Std+PBL: Same as Std, but with nudging toward temperature and moisture in PBL High+PBL: Same as High, but with nudging toward temperature and moisture in PBL 12 UTC 4 Aug – 00 UTC 25 Aug 2006 Four 5.5-day overlapping run segments

8. Preliminary Analysis Show mean error (“bias”) from 20-day runs  Keep it simple, for now  MAE and RMSE do not change “bottom line” Verify against ~700 NWS surface stations  Includes T, “Q”, wind, surface pressure  Included in analyses (i.e., nudged)  Includes urban, suburban, and rural sites Verify against 67 CASTNET observations  Includes T, RH, wind, SW radiation  Independent of analyses (i.e., not considered in nudging)  Largely rural sites

9. 2-m Temperature vs. CASTNET DailyBy Day in WRF Run

10. 2-m Dew Point vs. CASTNET DailyBy Day in WRF Run

11. SW Radiation vs. CASTNET DailyBy Day in WRF Run

12. 10-m Wind Speed vs. CASTNET DailyBy Day in WRF Run

13. Diurnal 2-m Temperature NudgedNot Nudged

14. Diurnal 2-m Mixing Ratio 2-m Q for CASTNET not included because CASTNET does not report surface pressure, which is required to convert RH to Q. NudgedNot Nudged

15. Diurnal 10-m Wind Speed NudgedNot Nudged

16. Diurnal 10-m Wind Direction NudgedNot Nudged

17. Summary Preliminary results suggest:  Nudging coefficients for wind, temperature, and moisture can be increased over Seaman et al. (1995), i.e., “Low” values, *if not obs nudging*  Stronger nudging, i.e., “High” values, reduces bias in 10-m wind speed, but has little impact on 2-m temperature and dew point.  Nudging toward moisture may still need to be weaker than towards temperature and wind.  Nudging toward temperature and moisture in the PBL increases the bias for 2-m temperature, 2-m dew point, 10-m wind speed, and shortwave radiation at CASTNET sites (not nudged).  As expected, statistics vs. NWS observations are better than statistics vs. CASTNET sites, which are independent of the analyses.  Model behavior with and without nudging in the PBL is vastly different during stable (nighttime) regime than convective (daytime) regime

18. Next Steps Further evaluation with current runs  Evaluation against upper-air observations, PBL heights, precipitation, etc.  Additional methods Repeat sensitivities with NAM-218 (12-km) input Vary nudging strength “by variable” Use observation nudging with analysis nudging Test with analysis (e.g., RAWINS) in WRF

19. Looking Ahead…2-m Temp. using NARR and NAM218 vs. CASTNET Uses 12-km NAM218 analyses; initialized 00 UTC 20 Jul Uses 32-km NARR analyses; initialized 12 UTC 4 Aug MAE and RMSE [K]

20. Acknowledgments Jonathan Pleim (NOAA) Lara Reynolds (CSC) Disclaimer: The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce's National Oceanic and Atmospheric Administration (NOAA) and under agreement number DW13921548. This work constitutes a contribution to the NOAA Air Quality Program. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reflect their policies or views.